2.1 Dataset - gapminder

Identify the target variable from gapminder dataset.

library(tidyverse)
library(gapminder)

gapminder %>% 
  DT::datatable()

2.2 Visualize

Select one country and visualize lifeExp trend over year.

gapminder %>% 
  filter(str_detect(country, "Korea, Rep")) %>% 
  ggplot(aes(x=year, y=lifeExp)) +
    geom_line() +
    geom_point()

Compare lifeExp with gdpPercap over year.

gapminder %>% 
  filter(str_detect(country, "Korea, Rep")) %>% 
  select(year, lifeExp, gdpPercap) %>% 
  gather(variable, value, -year) %>% 
  ggplot(aes(x=year, y=value)) +
    geom_line() +
    geom_point() +
    facet_wrap(~variable, scale="free")

Decorate through adding labels, title, and applying a theme.

library(extrafont)
loadfonts()

gapminder %>% 
  filter(str_detect(country, "Korea, Rep")) %>% 
  select(year, lifeExp, gdpPercap) %>% 
  gather(variable, value, -year) %>% 
  ggplot(aes(x=year, y=value)) +
    geom_line() +
    geom_point() +
    facet_wrap(~variable, scale="free") +
    labs(x="", y="", 
         title="GDP per capita and Life Expectancy over time",
         subtitle="Data from gapminder, Korea") +
    theme_bw(base_family = "NanumGothic")

3.1 tooltip

tooltip_p <- gapminder %>% 
     ggplot(aes(x=year, y=lifeExp, 
                group=country, 
                text=paste0("Continent: ", continent, "\n", 
                            "Country: ", country, "\n", 
                            "Life Expectancy: ", lifeExp))) + 
     geom_line()
 
ggplotly(tooltip_p, tooltip = "text")

3.2 Highlight

lifeExp_highlight <- highlight_key(gapminder, ~country)

highlight_p <- lifeExp_highlight %>% 
     ggplot(aes(x=year, y=lifeExp, 
                group=country, 
                text=paste0("Continent: ", continent, "\n", 
                            "Country: ", country, "\n", 
                            "Life Expectancy: ", lifeExp))) + 
     geom_line()
 
highlight_gg <- ggplotly(highlight_p, tooltip = "text")

highlight(highlight_gg, on = "plotly_click",
          selectize = TRUE,
          dynamic = TRUE,
          persistent = TRUE)

3.3 Shared Link

life_cjk <- gapminder %>% 
    filter(country %in% c("China", "Japan", "Korea, Rep.")) %>% 
    ggplot(aes(x=year, y=lifeExp, group=country, 
             text=paste0("Continent: ", continent, "\n", 
                         "Country: ", country, "\n", 
                         "Life Expectancy: ", lifeExp))) + 
    geom_line() +
    geom_point() +
    facet_wrap(~country)

ggplotly(life_cjk, tooltip = "text")

3.4 Shared Link - htmlwidgets

gapminder_cjk <- gapminder %>% 
  filter(country %in% c("China", "Japan", "Korea, Rep."))

gapminder_sd <- crosstalk::SharedData$new(gapminder_cjk)

## life expectancy by year
life_cjk <- gapminder_sd %>% 
  plot_ly(x = ~year, y= ~lifeExp, color=~country) %>% 
    add_markers() %>% 
    add_lines() %>% 
    highlight("plotly_selected", dynamic = TRUE)

gdp_cjk <- gapminder_sd %>% 
  plot_ly(x = ~gdpPercap, y= ~lifeExp) %>% 
    add_histogram2d() %>% 
    highlight("plotly_selected", dynamic = TRUE)

## bind two hetergeneous plots
crosstalk::bscols(life_cjk, gdp_cjk)

4.1 gganimate transition_time()

library(gganimate)
gapminder %>% 
  ggplot(aes(x=gdpPercap, y=lifeExp, group=country, color=country, 
             text=paste0("Continent: ", continent, "\n", 
                         "Country: ", country, "\n", 
                         "Life Expectancy: ", lifeExp))) + 
    geom_point(aes(size=gdpPercap)) +
    facet_wrap(~continent, ncol=2) +
    scale_x_sqrt() +
    theme(legend.position = "none") +
    transition_time(year) +
    labs(title = "Year: {frame_time}")